BIOMEDICAL IMAGING GROUP (BIG)
Laboratoire d'imagerie biomédicale (LIB)

JDLL: A Library to Run Deep Learning Models on Java Bioimage Informatics Platforms

C. García-López-de-Haro, S. Dallongeville, T. Musset, E. Gómez-de-Mariscal, D. Sage, W. Ouyang, A. Muñoz-Barrutia, J.-Y. Tinevez, J.-C. Olivo-Marin

Nature Methods, vol. 21, no. 1, pp. 7–8, January 2024.

The advancements in artificial intelligence (AI) technology over the past decade have been a breakthrough in imaging for life sciences, paving the way for novel methods in image restoration [1], reconstruction [2] and segmentation [3]. However, the wide adoption of deep learning (DL) techniques by end users in bioimage analysis is hindered by the complexity of their deployment. These techniques stem from a variety of rapidly evolving frameworks (for example, TensorFlow 1 or 2, PyTorch) that come with distinct and often conflicting setups, which can discourage even proficient developers. This has led to integration difficulties or even absence in mainstream bioimage informatics platforms such as ImageJ, Icy and Fiji, many of which are primarily developed in Java.

References

  1. M. Weigert, U. Schmidt, T. Boothe, A. Müller, A. Dibrov, A. Jain, B. Wilhelm, D. Schmidt, C. Broaddus, S. Culley, M. Rocha-Martins, F. Segovia-Miranda, C. Norden, R. Henriques, M. Zerial, M. Solimena, J. Rink, P. Tomancak, L. Royer, F. Jug, E.W. Myers , "Content-Aware Image Restoration: Pushing the Limits of Fluorescence Microscopy," Nature Methods, vol. 15, no. 12, pp. 1090-1101, December 2018.

  2. C. Belthangady, L.A. Royer, "Applications, Promises, and Pitfalls of Deep Learning for Fluorescence Image Reconstruction," Nature Methods, vol. 16, pp. 1215-1225, December 2019.

  3. E. Moen, D. Bannon, T. Kudo, W. Graf, M. Covert, D. Van Valen, "Deep Learning for Cellular Image Analysis," Nature Methods, vol. 16, pp. 1233-1246, December 2019. 

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AUTHOR="Garcia-L{\'{o}}pez-de-Haro, C. and Dallongeville, S. and Musset,
	T. and G{\'{o}}mez-de-Mariscal, E. and Sage, D. and Ouyang, W. and
	Mu{\~{n}}oz-Barrutia, A. and Tinevez, J.-Y. and Olivo-Marin, J.-C.",
TITLE="{JDLL}: {A} Library to Run Deep Learning Models on {J}ava
	Bioimage Informatics Platforms",
JOURNAL="Nature Methods",
YEAR="2024",
volume="21",
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pages="7--8",
month="January",
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